Classical RS regard recommending as a static process mainly focusing on “the immediate feedback and do not consider long term reward” [1]}, [2]}. Instead, to achieve lasting CWB and the related educational processes, it is necessary to account for the effects of sequences of recommendations. For example, sequencing of lectures, tests, and feedback, is common in most educational strategies.
In addition, classical RS does not consider the interdependence between users’ preferences and the RS recommendations,
which is crucial to model and counter the filter bubble and echo chamber phenomena.
Another reason for the CWB-RS to consider a temporal dimension is to enable the use of an accurate dynamic model of the students and the natural variation of their preferences [3]}.
This allows, for example, to prepare the conditions and select the best time for exposure to content aimed at improving students' empathy as well as avoiding wrong conditions, such as those with a high level of user stress, when such content would be ignored or even lead to backfire [4]}.
